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Why MIT switched from Scheme to Python (2009)

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284 points borski | 3 comments | 25 Jul 25 16:38 UTC | HN request time: 0.652s | source
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MontyCarloHall ◴[25 Jul 25 17:23 UTC] No.44685710[source]▶
Isn't this just part of the broader trend of CS departments switching away from teaching computer science to teaching computer engineering, which in turn is part of the more general trend of colleges becoming more vocational?

LISP dialects like Scheme are excellent for teaching pure computer science because they are the closest thing to executing lambda calculus expressions. Whereas Python is excellent for teaching applied computer engineering, because it's essentially executable pseudocode for imperative languages, and imperative languages are the most common language a computer engineer encounters in the real world.

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SoftTalker ◴[25 Jul 25 17:31 UTC] No.44685819[source]▶
Yes. One of the biggest complaints that computer science departments used to get from students is that they weren't learning any languages that employers are using.
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90s_dev ◴[25 Jul 25 17:52 UTC] No.44686076[source]▶
To be fair, if you learn computer science well enough to thoroughly understand Scheme, I don't think it'll take more than a few weeks during the summer to learn Python.
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kstrauser ◴[25 Jul 25 17:56 UTC] No.44686128[source]▶
I disagree. You can learn the language itself pretty quickly. Finding your way through the expansive standard library will take longer. Getting a good handle on the package ecosystem is a lifetime learning project.
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acuozzo ◴[25 Jul 25 18:17 UTC] No.44686393[source]▶
Knowing your way around the ecosystem of one programming language does not build up the intuition necessary for identifying O(n²) (or worse!) algorithms and choosing/writing O(log(n)) (or better!) ones instead.

Computer Science has little to do with science, but what it teaches you is certainly closer to science than just building a huge mental index for a bunch of work done by other people.

There's certainly value in that skill, but it has no place in a Computer Science curriculum.

This would be like taking Astrophysics students and telling them to study the details of all of the different kinds of telescopes they can buy.

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1. trothamel ◴[25 Jul 25 19:31 UTC] No.44687346[source]▶
> Knowing your way around the ecosystem of one programming language does not build up the intuition necessary for identifying O(n²) (or worse!) algorithms and choosing/writing O(log(n)) (or better!) ones instead.

I'll disagree with this, at least in terms of Scheme versus Python.

Python is visually close enough to other languages that the skills you develop to quickly see O(n²) algorithms easily transfer to many other languages. Scheme is very different visually, and so the intuition doesn't transfer as well. Sure, it's possible your intuition is wrong, but when scanning a program intuition can help in the first pass.

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2. anthk ◴[25 Jul 25 21:31 UTC] No.44688727[source]▶
Scheme has (trace), at least most interpreters (and his cousing Common Lisp) have some tracing and pretty-printing features. Far more powerful than anything Python could offer.

Oh, and of course it has functions like sdraw or draw-cons-tree when you can print the contents of a list in seconds as an ASCII-ART chart:

https://www.t3x.org/s9fes/draw-tree.scm.html

The file it's in the public domain.

Try that with Python.

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3. trothamel ◴[25 Jul 25 21:48 UTC] No.44688899[source]▶
Python has the pprint module, which takes care of this for you, and for more datatypes than are done here. (I don't see how this would handle a hashmap in a sensible way.)

But I'm not sure how this addresses what I was saying, which is that the intuitions about algorithms you get working on Python are easier to transfer to popular languages like C++, Java, Javascript, Rust, etc..